Utilizing insights of DNA repair machinery to discover MMEJ deletions and novel mechanisms
Bibliographic record
Abstract
We developed Del-read, an algorithm targeting medium-sized deletions (6-100 bp) in short-reads, which are challenging for current variant callers relying on alignment. Our focus was on Micro-Homolog mediated End Joining deletions (MMEJ-dels), prevalent in myeloid malignancies. MMEJ-dels follow a distinct pattern, occurring between two homologies, allowing us to generate a comprehensive list of MMEJ-dels in the exome. Using Del-read, we identified numerous novel germline and somatic MMEJ-dels in BEAT-AML and TCGA-breast datasets. Validation in 672 healthy individuals confirmed their presence. These novel MMEJ-dels were linked to genomic features associated with replication stress, like G-quadruplexes and minisatellite. Additionally, we observed a new category of MMEJ-dels with an imperfect-match at the flanking sequences of the homologies, suggesting a mechanism involving mispairing in homology alignment. We demonstrated robustness of the repair system despite CRISPR/Cas9-induced mismatches in the homologies. Further analysis of the canonical ASXL1 deletion revealed a diverse array of these imperfect-matches. This suggests a potentially more flexible and error-prone MMEJ repair system than previously understood. Our findings highlight Del-read's potential in uncovering previously undetected deletions and deepen our understanding of repair mechanisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".